基于PSO-控制变量参数化混合策略的间歇化工过程优化控制

控制变量参数化方法作为一种化工过程动态优化的梯度搜索算法，其求解效率过于依赖初始给定轨迹。目前初始轨迹一般都是设定在边界值或中间值，缺乏科学依据，从而大大影响了算法的收敛速度。针对这一问题，提出了一种粒子群优化(PSO)与控制变量参数化方法混合的策略，首先利用粒子群优化对间歇化工过程最优控制量进行求解，结果作为控制变量参数化方法初始给定轨迹，进行二次优化。双层优化的混合策略提高了控制变量参数化方法的收敛速度和粒子群优化算法的求解精度。将混合策略应用于两个间歇化工过程优化控制实例，仿真结果表明了该算法对求解化工过程动态优化问题具有可行性和有效性。     

基于遗传算法和粒子群优化算法的电力系统无功优化

从数学的角度分析,电力系统无功优化是一个多变量、多约束、非连续性的混合非线性规划问题,因此,优化过程十分复杂.以减少有功网损为目标函数建立电力系统无功优化计算的数学模型,基于遗传算法和粒子群优化算法,提出一种新颖的混合策略来求解无功优化问题.IEEE 6和IEEE 14节点系统的仿真计算结果表明:与单一的遗传算法或粒子群优化算法相比,该混合策略在优化效果方面具有明显的优势.     

自适应混合粒子群约束优化算法及其在软测量模型参数估计中的应用

对于软测量模型参数估计问题, 针对传统梯度法求解非线性最小二乘模型时依赖初值、需要追加趋势分析进行验证和无法直接求解复杂问题的缺陷, 提出将参数估计化为约束优化问题, 使用混合优化算法求解的新思路。为此提出一种自适应混合粒子群约束优化算法(AHPSO-C)。在AHPSO-C算法中, 为平衡全局搜索(混沌粒子群)和局部搜索(内点法), 引入自适应内点法最大函数评价次数更新策略。对12个经典测试函数的仿真结果表明, AHPSO-C是求解约束优化问题的一种有效算法。将算法用于淤浆法高密度聚乙烯(HDPE)串级反应过程中熔融指数软测量模型参数估计, 验证了方法的可行性与优越性。     

Optimization-based design of reactive distillation columns using a memetic algorithm

The design optimization of reactive distillation columns (RDC) is characterized by complex nonlinear constraints, nonlinear cost functions, and the presence of many local optima. The standard approach is to use MINLP solvers that work on a superstructure formulation where structural decisions are represented by discrete variables and lead to an exponential increase in the computational effort. The mathematical programming (MP) methods which solve the continuous sub-problems provide only one local optimum which depends strongly on the initialization. In this contribution a memetic algorithm (MA) is introduced and applied to the global optimization of four different formulations of a computational demanding real-world design problem. An evolution strategy addresses the global optimization of the design decisions, while continuous sub-problems are efficiently solved by a robust MP solver. The MA is compared to MINLP techniques. It is the only algorithm that finds the global solution in reasonable times for all model formulations.     

换热网络合成问题的并行BB/SQP混合算法

换热网络合成问题通常可用非凸、非线性、不可微的混合整数非线性规划模型描述。基于GPU的并行计算技术为求解大规模模型提供了高效支撑。针对已有并行SQP算法求解换热网络合成问题中存在二元变量组合数过多、并行SQP算法求解结果严重依赖初值等问题,提出了BB/SQP混合并行算法。该算法采用BB算法代替枚举法,不但大大减少了模型求解中可能的二元变量组合,而且为SQP算法选出了可行的初值,从而提高了算法的求解质量。研究表明,所提出的混合并行算法能够有效求解换热网络合成问题,且并行计算相比串行计算的求解速度显著增加,加速比可达39。     

基于遗传算法和有限元方法的

以遗传算法作为工艺条件的优化方法，以化学反应过程的有限元模拟方法作为目标函数的求解方法，可以建立化学工艺的优化系统。以硅橡胶材料的硫化反应为例，建立了面向设定值的反应效率优化模型，确定了相关的决策变量，叙述了优化问题的求解步骤。最后通过典型算例展示了工艺条件的优化过程。     

A centroid‐based sampling strategy for kriging global modeling and optimization

A new sampling strategy is presented for kriging‐based global modeling. The strategy is used within a kriging/response surface (RSM) algorithm for solving NLP containing black‐box models. Black‐box models describe systems lacking the closed‐form equations necessary for conventional gradient‐based optimization. System optima can be alternatively found by building iteratively updated kriging models, and then refining local solutions using RSM. The application of the new sampling strategy results in accurate global model generation at lower sampling expense relative to a strategy using randomized and heuristic‐based sampling for initial and subsequent model construction, respectively. The new strategy relies on construction of an initial kriging model built using sampling data obtained at the feasible region's convex polytope vertices and centroid. Updated models are constructed using additional sampling information obtained at Delaunay triangulation centroids. The new sampling algorithm is applied within the kriging‐RSM framework to several numerical examples and case studies to demonstrate proof of concept. © 2009 American Institute of Chemical Engineers AIChE J, 2010     

Flexibility index of black-box models with parameter uncertainty through derivative-free optimization

The existing methods of flexibility index are mainly based on mixed-integer linear or nonlinear programming methods, making it difficult to readily deal with complex mathematical models. In this article, a novel solution strategy is proposed for finding a reliable upper bound of the flexibility index where the process model is implemented in a black box that can be directly executed by a commercial simulator, and also avoiding the need for calculating derivatives. Then, the flexibility index problem is formulated as a sequence of univariate derivative-free optimization (DFO) models. An external DFO solver based on trust-region methods can be called to solve this model. Finally, after calculating the critical point of the model parameters, the vertex enumeration method and two gradient approximation methods are proposed to evaluate the impact of process parameters and to evaluate the flexibility index. A reaction model is studied to show the efficiency of the proposed algorithm.     

Multi-objective modeling and optimization for scheduling of cracking furnace systems

Cracking furnace is the core device for ethylene production. In practice, multiple ethylene furnaces are usual y run in parallel. The scheduling of the entire cracking furnace system has great significance when multiple feeds are simultaneously processed in multiple cracking furnaces with the changing of operating cost and yield of product. In this paper, given the requirements of both profit and energy saving in actual production process, a multi-objective optimization model contains two objectives, maximizing the average benefits and minimizing the average coking amount was proposed. The model can be abstracted as a multi-objective mixed integer non-linear programming problem. Considering the mixed integer decision variables of this multi-objective problem, an improved hybrid encoding non-dominated sorting genetic algorithm with mixed discrete variables (MDNSGA-I ) is used to solve the Pareto optimal front of this model, the algorithm adopted crossover and muta-tion strategy with multi-operators, which overcomes the deficiency that normal genetic algorithm cannot handle the optimization problem with mixed variables. Finally, using an ethylene plant with multiple cracking furnaces as an example to illustrate the effectiveness of the scheduling results by comparing the optimization results of multi-objective and single objective model.     

An ESDIRK method with sensitivity analysis capabilities

A new algorithm for numerical sensitivity analysis of ordinary differential equations (ODEs) is presented. The underlying ODE solver belongs to the Runge–Kutta family. The algorithm calculates sensitivities with respect to problem parameters and initial conditions, exploiting the special structure of the sensitivity equations. A key feature is the reuse of information already computed for the state integration, hereby minimizing the extra effort required for sensitivity integration. Through case studies the new algorithm is compared to an extrapolation method and to the more established BDF based approaches. Several advantages of the new approach are demonstrated, especially when frequent discontinuities are present, which renders the new algorithm particularly suitable for dynamic optimization purposes.     

基于偏最小二乘隐变量空间的控制器设计方法

基于偏最小二乘（PLS）隐变量空间的控制利用PLS算法内部关系主元独立的结构,可以实现自动解耦多变量之间的严重相关性,且自动实现隐变量变量配对,从而使得MIMO控制简化为SISO控制。由于PLS为稳态算法,要进行动态PLS算法才能与控制要求相符。但是因为PLS隐变量之间并不能完全消除相关性,因此如果采用PID控制器,在整定时回路之间将相互影响且控制器参数不稳定,由此进行了基于动态PLS隐变量空间模型的最优化控制。同时就加压网前箱和一个蒸馏器给出了该控制方法的仿真应用,显示了该方法的特性。     

基于简约空间序列二次规划算法和混合求导方法的精馏塔操作优化

以开放式方程建立精馏塔严格机理模型,对精馏塔的操作优化进行了研究.根据精馏塔操作优化命题自由度低、模型结构稀疏且导数难以解析求解的特点,提出了基于简约空间序列二次规划算法（SQP）和混合求导方法相结合的精馏塔操作优化计算方法.在该方法中优化命题采用简约空间SQP算法求解,求解过程中需要的导数信息采用解析表达和预处理的自动微分技术求取.计算结果表明,本文方法在计算效率上大大高于基于差分求导的标准SQP算法,有利于在线实时优化的实施.另外本文优化结果也表明,精馏塔操作优化对提高产品产量、提高综合经济效益具有明显作用.     

Alternatives for multiobjective optimization of a polymerization process

The optimal control policies for a polymerization process, particularly for batch free‐radical polymerization of methyl methacrylate, were determined using a multiobjective optimization technique. The process objectives considered in the optimization include monomer conversion, polydispersity index, polymerization degree, and total reaction time, weighted and combined in a scalar objective function. The decision variables were the initial concentration of the initiator and the temperature represented by isothermal steps. For solving the optimization problem, several methods based on sequential quadratic programming and a genetic algorithm were used and compared. Combining them into a hybrid method (the genetic algorithm provided the initial values for the traditional iterative method) led to the best results. The aims of this study were to develop an approach for optimizing the polymerization process and to describe alternatives for formulating and solving this problem, emphasizing the importance of user decision in choosing solutions based on technological criteria. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 100: 3680–3695, 2006     

Optimization of industrial CSTR for vinyl acetate polymerization using novel shuffled frog leaping based hybrid algorithms and dynamic modeling

Industrial scale continuous stirred tank reactor (CSTR) for production and grade transitions of poly vinyl acetate (PVAc) at the different reactor sizes was investigated. Such reactor is known to show oscillatory behavior and to have periodic limit points, particularly at high molecular weights. Four efficient novel hybrid optimization methods which use variable population size genetic algorithm (VPGA), bacterial optimization algorithm (BO) and shuffled frog leaping method (SFL) were introduced for this kind of reactors. These algorithms can reliably find dynamically stable points with desired conditions for PVAc production. The dynamic modeling combined with the new hybrid optimization methods were used to obtain the optimal operating conditions which increased the outlet flow rate by 17%.     

考虑初始操作温度影响的间歇放热反应系统动态柔性分析

It is shown in this article that by changing the initial operation condition of the batch processes, the dynamic performance of the system can be varied largely, especially for the initial operational temperature of the exothermic reaction. The initial operation condition is often ignored in the designing batch processes for flexibility against disturbances or parameter variations. When the initial condition is not rigid as in the case of a batch reactor, where the initial reaction temperature is quite arbitrary, optimization can also be applied to determine the ＂best＂ initial condition to use. Problems for dynamic flexibility analysis of exothermic reaction including initial temperature and process operation can be formulated as dynamic optimization problems. Formulations are derived when the initial conditions are considered or not. When the initial conditions are considered, the initial condition can be transferred into control variables in the first optimal step. The solution of the dynamic optimization is on the basis of Rugge-Kutta integration algorithm and decomposition search algorithm. This method, as illustrated and tested with two highly nonlinear process problems, enables the determination of the optimal level. The dynamic performance is improved by the proposed method in the two exothermic reaction examples.     

Iterative optimization of the economic efficiency of an industrial process within the validity area of the static plant model and its application to a Pulp Mill

Optimization of the steady state economic efficiency of an industrial process is a specific task because the decision variables of the optimization (setpoints of the control system) affect the process through the control strategy. Thus, the effects of saturation of a control system must be taken into account when the gradient of the objective function is estimated and the necessary optimality conditions are checked. In particular, because the optimality conditions cannot be checked directly in the presence of active constraints on the manipulated variables, approximations of the steady state values of the manipulated variables as functions of the setpoints (static plant model) are needed in order to be able to evaluate the optimality conditions. In this paper an iterative method for optimization of the plant profit rate is proposed avoiding the control saturation and is applied to the Pulp Mill benchmark model optimization. Three different static models describing the steady state values of the manipulated variables are constructed and used in the optimization. The results of the optimization are presented and compared against the straightforward single-step optimization of the plant economic efficiency.     

智能约束处理技术综述

首先时函数约束优化问题进行数学描述,分析其求解难点,然后综述当今主流的一些智能约束处理技术.包括罚函数法、基于排序的方法、基于可行性规则的方法、基于多目标的方法、特殊算子、基于译码器的方法、基于文化算法的技术、修补技术以及混合策略等,最后提出了有待进一步研究的几个方面.     

水煤浆气化装置操作优化技术及其应用

针对水煤浆气化装置操作优化问题,提出了一种多种群竞争型协同文化差分进化算法(MCCDE),算法中建立了基于差分进化算法的竞争型协同策略及竞争适应度评判方法,并引入了文化算法的部分思想。同时,建立了德士古气化炉操作优化模型,将MCCDE算法用于优化模型的求解。采用某化工厂气化系统实际运行数据进行仿真,经过操作优化计算,能够获得优化的控制参数,并提高气化炉有效气产率。最后,开发了水煤浆气化操作优化系统应用软件,能够实现将建模、控制、优化技术应用于实际生产中,以提高装置的经济效益。     

Optimal reaction conditions for the minimization of energy consumption and by‐product formation in a poly(ethylene terephthalate) process

We determined the optimal reaction conditions to minimize the energy cost and the quantities of by‐products for a poly(ethylene terephthalate) process by using the iterative dynamic programming (IDP) algorithm. Here, we employed a sequence of three reactor models: the semibatch transesterification reactor model, the semibatch prepolymerization reactor model, and the rotating‐disc‐type polycondensation reactor model. We selectively chose or developed the reactor models by incorporating experimentally verified kinetic models reported in the literature. We established the model for the entire reactor system by connecting the three reactor models in series and by resolving some joint problems arising when different types of reactor models were interconnected. On the basis of the simulation results of the reactor system, we scrutinized the cause and effect between the reaction conditions and the final quality of the polymer product. Here, we set up the optimization strategy by using IDP on the basis of the integrated reactor model, and the process variables with significant influence on the properties of polymer were selected as control variables with the help of a simulation study. With this method, we could refine the reaction conditions at the end of each iteration step by contracting the spectra of control regions, and the iteration process finally stopped when the profile of the optimal trajectory converged. We also took the constraints on the control variables into account to guarantee polymer quality and to suppress side reactions. Constituting six different strategies by setting weighting vectors differently, we examined the differences in optimal trajectories, the trend of optimality, and the quality of the final polymer product. For each of the strategies, we conducted the optimization to examine whether the number‐average degree of polymerization approached the desired value. © 2002 Wiley Periodicals, Inc. J Appl Polym Sci 86: 993–1008, 2002     

Hybrid biogeography based optimization for constrained optimal spot color matching

Biogeography‐based optimization (BBO) is a new evolutionary algorithm which mimics the immigration and emigration of species among islands. Used widely in packaging and printing to obtain a colorful appearance, the spot color matching (SCM) is formulated as a complex multi‐dimensional optimization problem. In this article, BBO is combined with the harmony search (HS) and opposition‐based learning (OBL) approaches to construct an effective hybrid algorithm for solving the SCM problem. HS is used to enhance the local searching ability of BBO, and OBL is employed to increase the diversity of initial population; consequently, the exploration and exploitation abilities of the hybrid algorithm are enhanced and well balanced. Experiment results are presented to show the effectiveness of the proposed scheme. © 2013 Wiley Periodicals, Inc. Col Res Appl, 39, 607–615, 2014